@incollection{RojahnAmbrosBiruetal.2023,
  author    = {Rojahn, Marcel and Ambros, Maximilian and Biru, Tibebu and Krallmann, Hermann and Gronau, Norbert and Grum, Marcus},
  title     = {Adequate basis for the data-driven and machine-learning-based identification},
  series = {Artificial intelligence and soft computing},
  booktitle = {Artificial intelligence and soft computing},
  editor    = {Rutkowski, Leszek and Scherer, Rafał and Korytkowski, Marcin and Pedrycz, Witold and Tadeusiewicz, Ryszard and Zurada, Jacek M.},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-42504-2},
  doi       = {10.1007/978-3-031-42505-9_48},
  pages     = {570 -- 588},
  year      = {2023},
  abstract  = {Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.},
  language  = {en}
}
@incollection{GronauRojahn2021,
  author    = {Gronau, Norbert and Rojahn, Marcel},
  title     = {Das Industrial Internet of Things (IIOT)},
  series = {IT-Recht : Recht, Wirtschaft und Technik der digitalen Transformation},
  booktitle = {IT-Recht : Recht, Wirtschaft und Technik der digitalen Transformation},
  editor    = {Leupold, Andreas and Wiebe, Andreas and Glossner, Silke},
  edition   = {4., {\"u}berarbeitet und erweitert},
  publisher = {C.H. Beck},
  address   = {M{\"u}nchen},
  isbn      = {978-3-406-74458-7},
  pages     = {1115 -- 1124},
  year      = {2021},
  language  = {de}
}